1,721,247 research outputs found
General-purpose audio tagging from noisy labels using convolutional neural networks
Full text linkGeneral-purpose audio tagging refers to classifying sounds that are
of a diverse nature, and is relevant in many applications where
domain-specific information cannot be exploited. The DCASE 2018
challenge introduces Task 2 for this very problem. In this task, there
are a large number of classes and the audio clips vary in duration.
Moreover, a subset of the labels are noisy. In this paper, we propose
a system to address these challenges. The basis of our system is
an ensemble of convolutional neural networks trained on log-scaled
mel spectrograms. We use preprocessing and data augmentation
methods to improve the performance further. To reduce the effects
of label noise, two techniques are proposed: loss function weighting
and pseudo-labeling. Experiments on the private test set of this task
show that our system achieves state-of-the-art performance with a
mean average precision score of 0.95
Consistent dictionary learning for signal declipping
Full text linkClipping, or saturation, is a common nonlinear distortion in
signal processing. Recently, declipping techniques have been proposed
based on sparse decomposition of the clipped signals on a fixed dictionary,
with additional constraints on the amplitude of the clipped samples.
Here we propose a dictionary learning approach, where the dictionary
is directly learned from the clipped measurements. We propose a soft-consistency
metric that minimizes the distance to a convex feasibility
set, and takes into account our knowledge about the clipping process.
We then propose a gradient descent-based dictionary learning algorithm
that minimizes the proposed metric, and is thus consistent with the clipping
measurement. Experiments show that the proposed algorithm outperforms
other dictionary learning algorithms applied to clipped signals.
We also show that learning the dictionary directly from the clipped signals
outperforms consistent sparse coding with a fixed dictionary
Improving reverberant speech separation with binaural cues using temporal context and convolutional neural networks
Full text linkGiven binaural features as input, such as interaural level difference
and interaural phase difference, Deep Neural Networks (DNNs)
have been recently used to localize sound sources in a mixture of speech
signals and/or noise, and to create time-frequency masks for the estimation
of the sound sources in reverberant rooms. Here, we explore a
more advanced system, where feed-forward DNNs are replaced by Convolutional
Neural Networks (CNNs). In addition, the adjacent frames
of each time frame (occurring before and after this frame) are used to
exploit contextual information, thus improving the localization and separation
for each source. The quality of the separation results is evaluated
in terms of Signal to Distortion Ratio (SDR)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
